We evaluate our model on two widely-used public benchmarks: the V-COCO (Verbs in COCO) [1]} and HICO-DET [2]} datasets.
V-COCO is a subset of COCO composed of 5,400 trainval images and 4,946 test images.
For V-COCO dataset, we report the \(\text{AP}_{\text{role}}\)  over 25 interactions in two scenarios.
HICO-DET contains 37,536 and 9,515 images for each training and test split with annotations for 600 \(\langle verb, object \rangle \)  interaction types.
We follow the previous settings and report the mAP over two evaluation settings (Default and Known Object), each with three different category sets: (1) all 600 HOI categories in HICO (Full), (2) 138 HOI categories with less than 10 training instances (Rare), and (3) 462 HOI categories with 10 or more training instances (Non-Rare).
See Appendix for details of the evaluation settings.
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